similar to: how to get means and confidence limits after glmmPQL or lmer

Hi folks! I run the following code to get a CI for a Poisson with lambda=12.73 library(MASS) set.seed(125) x <- rpois(100,12.73) lambda_hat<-fitdistr(x, dpois, list(lambda=12))$estimate #Confidence Intervals - Normal Approx. alpha<-c(.05,.025,.01) for(n in 1:length(alpha)) { LowerCI<-mean(x)-(qnorm(1-alpha[n]/2, mean = 0, sd = 1)*sqrt(var(x)/length(x)))

Here I am again with question I'll feel foolish for asking, when I see the answer. I'm trying to produce a report and here's where I get stuck: How do I get R2HTML to produce the same number format? Particularly remove the decimal places for Par and Sal. Are there better methods to produce this type of report? Thanks, L.A. R version 2.10.0 XP

Hi, R 1.0.1, on Linux(mandrake/RedHat). I am trying to write my first function using R and I need help figuring out the error I have indicated in the Subject line above. The purpose of the function is not to "wow" anyone, but just to serve as a stepping stone so that I might write more complicated functions in the future. First I created my function named "CIfunc" and saved

Hello fellow R people, I can not figure out a pretty way to use strplit with vectors Imagine that I got the following data from someone with ID's representing several factors ID data A1-B1-t1 0 A1-B1-t2 1 A1-B2-t1 5 A1-B2-t2 10 A1-B10-t1 0 A1-B10-t2 1 A1-B20-t1 5 A1-B20-t2 10 ... I would like to turn this dataframe to station substation time data A1 B1 t1 0 A1 B1 t2 1 A1 B2 t1 5

Forwarded Message: > To: Steve Arthur <sarthur at crick.protogene.com> > From: Thomas Lumley <thomas at biostat.washington.edu> > Subject: Re: [R] couldn't find function "..." > Date: Wed, 5 Jul 2000 16:16:25 -0700 (PDT) > ----- > On Wed, 5 Jul 2000, Steve Arthur wrote: > > > Hi, > > > > R 1.0.1, on Linux(mandrake/RedHat).

Hi everyone, I have posted a similar question to this list, but I don't get a reply. I really want to solve this problem, so I post it again... I am trying to use R to fit some mixed-effects models for a nested data. The data is a simulated data with 111 subjects. Each subject has 6 waves' data. Below are the first two subjects' data : > simu1[1:12,] Grouped Data: gf ~ age |

A long formula which is converted using as.character, looses its last part: ``diagonal = 1e-12)'' Shorter formula is ok though. Best, H??vard ************ Browse[2]> formula.str y ~ -1 + b1 + b2 + b3 + b4 + b5 + b6 + b7 + b8 + b9 + b10 + b11 + b12 + b13 + b14 + b15 + b16 + b17 + b18 + b19 + b20 + b21 + b22 + b23 + b24 + b25 + b26 + b27 + b28 + b29 + b30 + b31 + b32 +

Another question about lattice: Is there a way to use plotmath in the labels and strips? E.g. xyplot(y~x|group,data=test.df, panel=function(x,y,subscripts,df) { llines(x,y,col="black",lwd=2) llines(x,df[subscripts,"lowerCI"],lty=2,col="#aaaaaa") llines(x,df[subscripts,"upperCI"],lty=2,col="#aaaaaa") },

Dear helpers, I wrote a code which estimates a multi-equation model with generalized least squares (GLS). I can use GLS because I know the covariance matrix of the residuals a priori. However, it is a bit slow and I wonder if anybody would be able to point out a way to make it faster (it is part of a bigger code and needs to run several times). Any suggestion would be greatly appreciated. Carlo

I'm hoping that this is a relatively easy question for someone familiar with the lme4 package. I'm accustomed to using HLM software and writing a simple 2 level [null] equation like this: L1 - Yij = b0 + e L2 - b0 = B00 + u0 The following command in R provides results that are identical to the HLM program. results <- lmer( Y ~ 1 |id , PanelData4) I can't

> > If one compares the random effect estimates, in fact, one sees that > > they are in the correct proportion, with the expected signs. They are > > just approximately eight orders of magnitude too small. Is this a bug? > > BLUPs are essentially shrinkage estimates, where shrinkage is > determined with magnitude of variance. Lower variance more > shrinkage towards

I am fitting models to the responses to a questionnaire that has seven yes/no questions (Item). For each combination of Subject and Item, the variable Response is coded as 0 or 1. I want to include random effects for both Subject and Item. While I understand that the datasets are fairly small, and there are a lot of invariant subjects, I do not understand something that is happening here, and in

Dear R listers, I know I'm breaking the rules by asking a "homework" related question-- I hope you'll forgive me. I am a social psychology graduate student, and the only one in my department who uses R. I successfully completed my multiple regression and structural equation modeling courses using R (John Fox's car and sem packages were a big help, as was his book).

Hi, I am using Splus to run a multiphase mixed-effects model. The quations of the models are as below: gf[ij]=b0[i]+b1[i]*age[ij]+b2[i]*max(0,(age[ij]-tau[i]))^2+e[ij] b0[i]=b00+e[i0] b1[i]=b10+e[i1] b2[i]=b20+e[i2] tau[i]=tau+e[i3] i: 1,2,...,100 subjects j: 1,2,...,6 occasions The main scripts of Splus is: simu1<-groupedData(gf~age|id) simu.nlme<-nlme(gf~(b0 + b1 * age + b2 *

Hi, Sorry to bother those who are not interested in the question. I am using R to run a multiphase mixed-effects model. I simulated a data set by using the following model. And now I want to use R to estimate the parameters and compare the results with the true values. The equations of the models are as below: gf[ij]=b0[i]+b1[i]*age[ij]+b2[i]*max(0,(age[ij]-tau[i]))+e[ij] b0[i]=b00+e[i0]

I am fitting models to the responses to a questionnaire that has seven yes/no questions (Item). For each combination of Subject and Item, the variable Response is coded as 0 or 1. I want to include random effects for both Subject and Item. While I understand that the datasets are fairly small, and there are a lot of invariant subjects, I do not understand something that is happening

CentOS Errata and Bugfix Advisory 2019:2597 Upstream details at : https://access.redhat.com/errata/RHBA-2019:2597 The following updated files have been uploaded and are currently syncing to the mirrors: ( sha256sum Filename ) x86_64: 8ac122b3ed2b22896383b00fe22b5ca09c20cff82f4d98dfb4074c45a43391d7 java-1.8.0-openjdk-1.8.0.222.b10-1.el7_7.i686.rpm

CentOS Errata and Security Advisory 2019:1815 Moderate Upstream details at : https://access.redhat.com/errata/RHSA-2019:1815 The following updated files have been uploaded and are currently syncing to the mirrors: ( sha256sum Filename ) x86_64: 4db61ec83854e045dc727d41be296d2635e5a66cca3812ca8ba125ed5e2963ea java-1.8.0-openjdk-1.8.0.222.b10-0.el7_6.i686.rpm

CentOS Errata and Security Advisory 2018:1649 Important Upstream details at : https://access.redhat.com/errata/RHSA-2018:1649 The following updated files have been uploaded and are currently syncing to the mirrors: ( sha256sum Filename ) x86_64: ca83b9ef933d1ee8b508394690ad3d97306b3ab24afcd2f198893f4c45f9137d java-1.8.0-openjdk-1.8.0.171-8.b10.el7_5.i686.rpm

CentOS Errata and Security Advisory 2018:1191 Critical Upstream details at : https://access.redhat.com/errata/RHSA-2018:1191 The following updated files have been uploaded and are currently syncing to the mirrors: ( sha256sum Filename ) x86_64: dfd2bd4a32a3e9eec12dbac0f4e71ef141a8214a661b9de5e89a2215038d99c7 java-1.8.0-openjdk-1.8.0.171-7.b10.el7.i686.rpm